Papers will be published in a volume of Springer LNCS series (MICCAI 2017 Workshops Volume)

ML-CDS 2017 builds on the success of the last four events in this series. We are looking for high-quality submissions that address innovative research and development in the learning methods using multimodal medical data. Applications in clinical decision support and treatment planning are of highest interest. Experts in quantitative imaging, text analytics, and decision support systems will present to an audience of scientists and clinicians. Advances in the development and use of deep learning methods with medical imaging and text data are expected to be among major topics of submission and discussion at the event.

Tue, 2016-11-15 11:43

Ania Gawlik's article featured in the Best of the 2016 AUATuesday, November 15, 2016 - 11:43

Ania Gawlik's article was featured in a paper describing the best of AUA. The quoted text is as follows.

"In the 1970s there was great interest in prostate cancer cytology (based on needle aspiration) for prostate cancer diagnoses. It was supplanted after the pioneering work of Gleason demonstrated that the architecture of prostate cancer histology provided very significant prognostic information. History was reassessed in the report from Gawlik and coworkers,93who utilized computer image analysis of Feulgen and hematoxylin-eosin (H&E) nuclear features to predict BCR in men following RP; 69 patients (20 BCR and 49 nonrecurrences) were assessed with mean BCR-free survival time of 6.6 years and follow-up to 14 years. A total of 242 quantitative histomorphometric (QH) features describing nuclear shape, architecture, and disorder were calculated from the H&E and Feulgen-stained tissue microarray core images in each patient. The top 10 ranked features for each stain type were selected.

Gleason score did not discriminate between those who did or did not have BCR predictions using QH features extracted from Feulgen and H&E images and revealed statistically different outcomes. Combining Gleason score, H&E, and Feulgen together showed the highest classification accuracy (0.75; P < .001). Although this is a very small study, if validated, this may provide a fruitful arena for development—perhaps there is something new (again) under the sun."

“Identifying the Histomorphometric Basis of MRI Radiomic Features in Distinguishing Gleason Grades of Prostate Cancer”

Fri, 2016-11-04 10:11

New patent issued to CCIPD/BrIC labsFriday, November 4, 2016 - 10:11

US Patent US 9,483,822 "CO-OCCURRENCE OF LOCAL ANISOTROPIC GRADIENT ORIENTATIONS" has been issued with Co-inventors Anant Madabhushi, Prateek Prasanna and Pallavi Tiwari.

Abstract of the invention is as follows:-

Methods, apparatus, and other embodiments associated with distinguishing disease phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe) are described. One example apparatus includes a set of logics that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating disease pathology (e.g., cancer), computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, and classifies the phenotype of the disease pathology based on the feature vector. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image.

US Patent US 9,483,822 "CO-OCCURRENCE OF LOCAL ANISOTROPIC GRADIENT ORIENTATIONS" has been issued with Co-inventors Anant Madabhushi, Prateek Prasanna and Pallavi Tiwari.

Abstract of the invention is as follows:-

Methods, apparatus, and other embodiments associated with distinguishing disease phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe) are described. One example apparatus includes a set of logics that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating disease pathology (e.g., cancer), computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, and classifies the phenotype of the disease pathology based on the feature vector. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image.